Overview

Dataset statistics

Number of variables16
Number of observations5000
Missing cells1250
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory509.2 B

Variable types

Text1
Numeric7
Categorical6
DateTime1
Boolean1

Alerts

Approval_Status is highly overall correlated with Default_StatusHigh correlation
Default_Status is highly overall correlated with Approval_StatusHigh correlation
Age has 250 (5.0%) missing valuesMissing
Employment_Type has 250 (5.0%) missing valuesMissing
Annual_Income has 250 (5.0%) missing valuesMissing
Credit_Score has 250 (5.0%) missing valuesMissing
Utilization_Ratio has 250 (5.0%) missing valuesMissing
Approval_Status is uniformly distributedUniform
Customer_ID has unique valuesUnique

Reproduction

Analysis started2026-02-28 15:25:28.634942
Analysis finished2026-02-28 15:25:36.277417
Duration7.64 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Customer_ID
Text

Unique 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size268.7 KiB
2026-02-28T20:55:36.533962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters30000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5000 ?
Unique (%)100.0%

Sample

1st rowC00001
2nd rowC00002
3rd rowC00003
4th rowC00004
5th rowC00005
ValueCountFrequency (%)
c000091
 
< 0.1%
c050001
 
< 0.1%
c000011
 
< 0.1%
c000021
 
< 0.1%
c000031
 
< 0.1%
c000041
 
< 0.1%
c000051
 
< 0.1%
c000061
 
< 0.1%
c049851
 
< 0.1%
c049861
 
< 0.1%
Other values (4990)4990
99.8%
2026-02-28T20:55:36.868290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
07499
25.0%
C5000
16.7%
12500
 
8.3%
32500
 
8.3%
22500
 
8.3%
42500
 
8.3%
51501
 
5.0%
61500
 
5.0%
71500
 
5.0%
91500
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07499
25.0%
C5000
16.7%
12500
 
8.3%
32500
 
8.3%
22500
 
8.3%
42500
 
8.3%
51501
 
5.0%
61500
 
5.0%
71500
 
5.0%
91500
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07499
25.0%
C5000
16.7%
12500
 
8.3%
32500
 
8.3%
22500
 
8.3%
42500
 
8.3%
51501
 
5.0%
61500
 
5.0%
71500
 
5.0%
91500
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07499
25.0%
C5000
16.7%
12500
 
8.3%
32500
 
8.3%
22500
 
8.3%
42500
 
8.3%
51501
 
5.0%
61500
 
5.0%
71500
 
5.0%
91500
 
5.0%

Age
Real number (ℝ)

Missing 

Distinct45
Distinct (%)0.9%
Missing250
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean42.970105
Minimum21
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2026-02-28T20:55:36.978630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q132
median43
Q354
95-th percentile63
Maximum65
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.8979
Coefficient of variation (CV)0.30015983
Kurtosis-1.1767431
Mean42.970105
Median Absolute Deviation (MAD)11
Skewness0.028172286
Sum204108
Variance166.35581
MonotonicityNot monotonic
2026-02-28T20:55:37.101280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
38129
 
2.6%
62127
 
2.5%
43122
 
2.4%
59121
 
2.4%
46120
 
2.4%
36119
 
2.4%
33117
 
2.3%
22115
 
2.3%
32114
 
2.3%
37112
 
2.2%
Other values (35)3554
71.1%
(Missing)250
 
5.0%
ValueCountFrequency (%)
2192
1.8%
22115
2.3%
2397
1.9%
24101
2.0%
2598
2.0%
26108
2.2%
27102
2.0%
2897
1.9%
29107
2.1%
30103
2.1%
ValueCountFrequency (%)
65109
2.2%
64109
2.2%
63102
2.0%
62127
2.5%
6185
1.7%
6096
1.9%
59121
2.4%
5899
2.0%
57103
2.1%
5699
2.0%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size263.7 KiB
Male
2535 
Female
2465 

Length

Max length6
Median length4
Mean length4.986
Min length4

Characters and Unicode

Total characters24930
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male2535
50.7%
Female2465
49.3%

Length

2026-02-28T20:55:37.216941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T20:55:37.307929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male2535
50.7%
female2465
49.3%

Most occurring characters

ValueCountFrequency (%)
e7465
29.9%
a5000
20.1%
l5000
20.1%
M2535
 
10.2%
F2465
 
9.9%
m2465
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)24930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e7465
29.9%
a5000
20.1%
l5000
20.1%
M2535
 
10.2%
F2465
 
9.9%
m2465
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e7465
29.9%
a5000
20.1%
l5000
20.1%
M2535
 
10.2%
F2465
 
9.9%
m2465
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e7465
29.9%
a5000
20.1%
l5000
20.1%
M2535
 
10.2%
F2465
 
9.9%
m2465
 
9.9%

Employment_Type
Categorical

Missing 

Distinct4
Distinct (%)0.1%
Missing250
Missing (%)5.0%
Memory size282.9 KiB
Salaried
2595 
Self-Employed
1165 
Student
502 
Retired
488 

Length

Max length13
Median length8
Mean length9.0178947
Min length7

Characters and Unicode

Total characters42835
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalaried
2nd rowStudent
3rd rowSalaried
4th rowSalaried
5th rowSalaried

Common Values

ValueCountFrequency (%)
Salaried2595
51.9%
Self-Employed1165
23.3%
Student502
 
10.0%
Retired488
 
9.8%
(Missing)250
 
5.0%

Length

2026-02-28T20:55:37.452829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T20:55:37.533312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
salaried2595
54.6%
self-employed1165
24.5%
student502
 
10.6%
retired488
 
10.3%

Most occurring characters

ValueCountFrequency (%)
e6403
14.9%
a5190
12.1%
l4925
11.5%
d4750
11.1%
S4262
9.9%
r3083
7.2%
i3083
7.2%
t1492
 
3.5%
-1165
 
2.7%
f1165
 
2.7%
Other values (8)7317
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)42835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e6403
14.9%
a5190
12.1%
l4925
11.5%
d4750
11.1%
S4262
9.9%
r3083
7.2%
i3083
7.2%
t1492
 
3.5%
-1165
 
2.7%
f1165
 
2.7%
Other values (8)7317
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e6403
14.9%
a5190
12.1%
l4925
11.5%
d4750
11.1%
S4262
9.9%
r3083
7.2%
i3083
7.2%
t1492
 
3.5%
-1165
 
2.7%
f1165
 
2.7%
Other values (8)7317
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e6403
14.9%
a5190
12.1%
l4925
11.5%
d4750
11.1%
S4262
9.9%
r3083
7.2%
i3083
7.2%
t1492
 
3.5%
-1165
 
2.7%
f1165
 
2.7%
Other values (8)7317
17.1%

Annual_Income
Real number (ℝ)

Missing 

Distinct4747
Distinct (%)99.9%
Missing250
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1070163.2
Minimum150184
Maximum1999340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2026-02-28T20:55:37.642814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum150184
5-th percentile240474.65
Q1608272.25
median1059636.5
Q31535296.8
95-th percentile1906173.8
Maximum1999340
Range1849156
Interquartile range (IQR)927024.5

Descriptive statistics

Standard deviation534198.55
Coefficient of variation (CV)0.49917487
Kurtosis-1.2024626
Mean1070163.2
Median Absolute Deviation (MAD)464445
Skewness0.0081872253
Sum5.083275 × 109
Variance2.8536809 × 1011
MonotonicityNot monotonic
2026-02-28T20:55:37.770793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18521462
 
< 0.1%
15336842
 
< 0.1%
15562352
 
< 0.1%
14526071
 
< 0.1%
18309031
 
< 0.1%
15962241
 
< 0.1%
4866441
 
< 0.1%
10435461
 
< 0.1%
16333881
 
< 0.1%
17088281
 
< 0.1%
Other values (4737)4737
94.7%
(Missing)250
 
5.0%
ValueCountFrequency (%)
1501841
< 0.1%
1508141
< 0.1%
1513041
< 0.1%
1518251
< 0.1%
1523551
< 0.1%
1527811
< 0.1%
1531591
< 0.1%
1538251
< 0.1%
1538571
< 0.1%
1541221
< 0.1%
ValueCountFrequency (%)
19993401
< 0.1%
19980991
< 0.1%
19976341
< 0.1%
19970261
< 0.1%
19958041
< 0.1%
19953351
< 0.1%
19953041
< 0.1%
19949751
< 0.1%
19948041
< 0.1%
19945411
< 0.1%

Credit_Score
Real number (ℝ)

Missing 

Distinct551
Distinct (%)11.6%
Missing250
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean577.29516
Minimum300
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2026-02-28T20:55:37.907270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile327
Q1437
median580
Q3714
95-th percentile824
Maximum850
Range550
Interquartile range (IQR)277

Descriptive statistics

Standard deviation159.58018
Coefficient of variation (CV)0.27642736
Kurtosis-1.2016553
Mean577.29516
Median Absolute Deviation (MAD)139
Skewness-0.025274105
Sum2742152
Variance25465.832
MonotonicityNot monotonic
2026-02-28T20:55:38.034419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56917
 
0.3%
69817
 
0.3%
69417
 
0.3%
59216
 
0.3%
76116
 
0.3%
36516
 
0.3%
41616
 
0.3%
38216
 
0.3%
82316
 
0.3%
55216
 
0.3%
Other values (541)4587
91.7%
(Missing)250
 
5.0%
ValueCountFrequency (%)
3009
0.2%
3017
0.1%
3025
0.1%
30310
0.2%
3045
0.1%
3055
0.1%
3068
0.2%
3079
0.2%
3088
0.2%
30912
0.2%
ValueCountFrequency (%)
85013
0.3%
8498
0.2%
84810
0.2%
84712
0.2%
84613
0.3%
8458
0.2%
8448
0.2%
8434
 
0.1%
84211
0.2%
8416
0.1%

Loan_or_Credit_Amount
Real number (ℝ)

Distinct4987
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524066.54
Minimum50059
Maximum999958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2026-02-28T20:55:38.162575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50059
5-th percentile104005.7
Q1284851.25
median525681.5
Q3757740.75
95-th percentile950520.25
Maximum999958
Range949899
Interquartile range (IQR)472889.5

Descriptive statistics

Standard deviation273818.38
Coefficient of variation (CV)0.52248781
Kurtosis-1.2042578
Mean524066.54
Median Absolute Deviation (MAD)235630.5
Skewness0.006814769
Sum2.6203327 × 109
Variance7.4976506 × 1010
MonotonicityNot monotonic
2026-02-28T20:55:38.295786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4561382
 
< 0.1%
8096422
 
< 0.1%
8099452
 
< 0.1%
7464382
 
< 0.1%
7138732
 
< 0.1%
5527992
 
< 0.1%
2163732
 
< 0.1%
8402332
 
< 0.1%
2937622
 
< 0.1%
8651272
 
< 0.1%
Other values (4977)4980
99.6%
ValueCountFrequency (%)
500591
< 0.1%
502031
< 0.1%
504341
< 0.1%
505371
< 0.1%
505701
< 0.1%
506071
< 0.1%
508371
< 0.1%
510631
< 0.1%
510671
< 0.1%
512291
< 0.1%
ValueCountFrequency (%)
9999581
< 0.1%
9996691
< 0.1%
9994931
< 0.1%
9994471
< 0.1%
9994161
< 0.1%
9993271
< 0.1%
9987611
< 0.1%
9987581
< 0.1%
9979931
< 0.1%
9979551
< 0.1%

Credit_Limit
Real number (ℝ)

Distinct4991
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean796972.73
Minimum100157
Maximum1499812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2026-02-28T20:55:38.425004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100157
5-th percentile172146.25
Q1444586.75
median803178
Q31148774.2
95-th percentile1428405.3
Maximum1499812
Range1399655
Interquartile range (IQR)704187.5

Descriptive statistics

Standard deviation403598.15
Coefficient of variation (CV)0.506414
Kurtosis-1.2072307
Mean796972.73
Median Absolute Deviation (MAD)350424
Skewness-0.0017013596
Sum3.9848636 × 109
Variance1.6289146 × 1011
MonotonicityNot monotonic
2026-02-28T20:55:38.887821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14818152
 
< 0.1%
1424462
 
< 0.1%
12562202
 
< 0.1%
12037652
 
< 0.1%
5608942
 
< 0.1%
10336792
 
< 0.1%
5318132
 
< 0.1%
9875552
 
< 0.1%
13630592
 
< 0.1%
1430131
 
< 0.1%
Other values (4981)4981
99.6%
ValueCountFrequency (%)
1001571
< 0.1%
1003571
< 0.1%
1004431
< 0.1%
1005031
< 0.1%
1008191
< 0.1%
1011471
< 0.1%
1014951
< 0.1%
1016501
< 0.1%
1020751
< 0.1%
1032931
< 0.1%
ValueCountFrequency (%)
14998121
< 0.1%
14997171
< 0.1%
14993381
< 0.1%
14992861
< 0.1%
14991081
< 0.1%
14990171
< 0.1%
14982871
< 0.1%
14981701
< 0.1%
14981461
< 0.1%
14980081
< 0.1%

Utilization_Ratio
Real number (ℝ)

Missing 

Distinct91
Distinct (%)1.9%
Missing250
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean0.54785053
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2026-02-28T20:55:39.025363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.14
Q10.33
median0.55
Q30.77
95-th percentile0.95
Maximum1
Range0.9
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.25912269
Coefficient of variation (CV)0.47298063
Kurtosis-1.189186
Mean0.54785053
Median Absolute Deviation (MAD)0.22
Skewness-0.0035714876
Sum2602.29
Variance0.067144568
MonotonicityNot monotonic
2026-02-28T20:55:39.155113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8673
 
1.5%
0.3866
 
1.3%
0.5965
 
1.3%
0.3364
 
1.3%
0.1764
 
1.3%
0.8464
 
1.3%
0.5863
 
1.3%
0.3962
 
1.2%
0.5762
 
1.2%
0.562
 
1.2%
Other values (81)4105
82.1%
(Missing)250
 
5.0%
ValueCountFrequency (%)
0.125
 
0.5%
0.1161
1.2%
0.1256
1.1%
0.1359
1.2%
0.1453
1.1%
0.1549
1.0%
0.1656
1.1%
0.1764
1.3%
0.1854
1.1%
0.1950
1.0%
ValueCountFrequency (%)
127
0.5%
0.9948
1.0%
0.9845
0.9%
0.9757
1.1%
0.9644
0.9%
0.9555
1.1%
0.9448
1.0%
0.9339
0.8%
0.9251
1.0%
0.9151
1.0%

Number_of_Active_Accounts
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.515
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2026-02-28T20:55:39.247351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6888888
Coefficient of variation (CV)0.48048046
Kurtosis-1.2456166
Mean3.515
Median Absolute Deviation (MAD)1
Skewness-0.001575117
Sum17575
Variance2.8523455
MonotonicityNot monotonic
2026-02-28T20:55:39.325734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4870
17.4%
2865
17.3%
3835
16.7%
6826
16.5%
5825
16.5%
1779
15.6%
ValueCountFrequency (%)
1779
15.6%
2865
17.3%
3835
16.7%
4870
17.4%
5825
16.5%
6826
16.5%
ValueCountFrequency (%)
6826
16.5%
5825
16.5%
4870
17.4%
3835
16.7%
2865
17.3%
1779
15.6%

Past_Default_Count
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.3 KiB
3
1275 
0
1275 
1
1231 
2
1219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row0
4th row0
5th row3

Common Values

ValueCountFrequency (%)
31275
25.5%
01275
25.5%
11231
24.6%
21219
24.4%

Length

2026-02-28T20:55:39.423056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T20:55:39.504520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
31275
25.5%
01275
25.5%
11231
24.6%
21219
24.4%

Most occurring characters

ValueCountFrequency (%)
31275
25.5%
01275
25.5%
11231
24.6%
21219
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
31275
25.5%
01275
25.5%
11231
24.6%
21219
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
31275
25.5%
01275
25.5%
11231
24.6%
21219
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
31275
25.5%
01275
25.5%
11231
24.6%
21219
24.4%

Application_Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size275.7 KiB
Loan
2541 
Credit Card
2459 

Length

Max length11
Median length4
Mean length7.4426
Min length4

Characters and Unicode

Total characters37213
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoan
2nd rowCredit Card
3rd rowLoan
4th rowLoan
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Loan2541
50.8%
Credit Card2459
49.2%

Length

2026-02-28T20:55:39.601199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T20:55:39.664207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
loan2541
34.1%
credit2459
33.0%
card2459
33.0%

Most occurring characters

ValueCountFrequency (%)
a5000
13.4%
r4918
13.2%
C4918
13.2%
d4918
13.2%
L2541
6.8%
o2541
6.8%
n2541
6.8%
e2459
6.6%
i2459
6.6%
t2459
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)37213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a5000
13.4%
r4918
13.2%
C4918
13.2%
d4918
13.2%
L2541
6.8%
o2541
6.8%
n2541
6.8%
e2459
6.6%
i2459
6.6%
t2459
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)37213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a5000
13.4%
r4918
13.2%
C4918
13.2%
d4918
13.2%
L2541
6.8%
o2541
6.8%
n2541
6.8%
e2459
6.6%
i2459
6.6%
t2459
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)37213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a5000
13.4%
r4918
13.2%
C4918
13.2%
d4918
13.2%
L2541
6.8%
o2541
6.8%
n2541
6.8%
e2459
6.6%
i2459
6.6%
t2459
6.6%
Distinct599
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum2023-01-01 00:00:00
Maximum2024-08-22 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-28T20:55:39.762722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:39.902997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Region
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size271.2 KiB
Urban
2506 
Semi-Urban
1513 
Rural
981 

Length

Max length10
Median length5
Mean length6.513
Min length5

Characters and Unicode

Total characters32565
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowRural
3rd rowSemi-Urban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2506
50.1%
Semi-Urban1513
30.3%
Rural981
 
19.6%

Length

2026-02-28T20:55:40.027485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T20:55:40.094873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
urban2506
50.1%
semi-urban1513
30.3%
rural981
 
19.6%

Most occurring characters

ValueCountFrequency (%)
r5000
15.4%
a5000
15.4%
U4019
12.3%
b4019
12.3%
n4019
12.3%
S1513
 
4.6%
e1513
 
4.6%
m1513
 
4.6%
i1513
 
4.6%
-1513
 
4.6%
Other values (3)2943
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)32565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r5000
15.4%
a5000
15.4%
U4019
12.3%
b4019
12.3%
n4019
12.3%
S1513
 
4.6%
e1513
 
4.6%
m1513
 
4.6%
i1513
 
4.6%
-1513
 
4.6%
Other values (3)2943
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r5000
15.4%
a5000
15.4%
U4019
12.3%
b4019
12.3%
n4019
12.3%
S1513
 
4.6%
e1513
 
4.6%
m1513
 
4.6%
i1513
 
4.6%
-1513
 
4.6%
Other values (3)2943
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r5000
15.4%
a5000
15.4%
U4019
12.3%
b4019
12.3%
n4019
12.3%
S1513
 
4.6%
e1513
 
4.6%
m1513
 
4.6%
i1513
 
4.6%
-1513
 
4.6%
Other values (3)2943
9.0%

Default_Status
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
2500 
False
2500 
ValueCountFrequency (%)
True2500
50.0%
False2500
50.0%
2026-02-28T20:55:40.149176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Approval_Status
Categorical

High correlation  Uniform 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size278.4 KiB
Rejected
2500 
Approved
2500 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters40000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRejected
2nd rowApproved
3rd rowApproved
4th rowRejected
5th rowRejected

Common Values

ValueCountFrequency (%)
Rejected2500
50.0%
Approved2500
50.0%

Length

2026-02-28T20:55:40.231717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-28T20:55:40.301755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
rejected2500
50.0%
approved2500
50.0%

Most occurring characters

ValueCountFrequency (%)
e10000
25.0%
p5000
12.5%
d5000
12.5%
j2500
 
6.2%
R2500
 
6.2%
t2500
 
6.2%
c2500
 
6.2%
A2500
 
6.2%
r2500
 
6.2%
o2500
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)40000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e10000
25.0%
p5000
12.5%
d5000
12.5%
j2500
 
6.2%
R2500
 
6.2%
t2500
 
6.2%
c2500
 
6.2%
A2500
 
6.2%
r2500
 
6.2%
o2500
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)40000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e10000
25.0%
p5000
12.5%
d5000
12.5%
j2500
 
6.2%
R2500
 
6.2%
t2500
 
6.2%
c2500
 
6.2%
A2500
 
6.2%
r2500
 
6.2%
o2500
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)40000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e10000
25.0%
p5000
12.5%
d5000
12.5%
j2500
 
6.2%
R2500
 
6.2%
t2500
 
6.2%
c2500
 
6.2%
A2500
 
6.2%
r2500
 
6.2%
o2500
 
6.2%

Interactions

2026-02-28T20:55:35.067699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:30.226110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:31.000031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:32.316015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.011013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.683951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.393561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:35.161427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:30.363037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:31.614461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:32.407752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.101218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.782363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.482608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:35.268517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:30.478947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:31.759864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:32.516528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.205411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.893841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.595367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:35.367882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:30.580288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:31.868812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:32.615361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.302426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.998083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.687216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:35.467064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:30.677963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:31.977513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:32.719718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.398912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.099192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.778253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:35.571763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:30.783548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:32.092612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:32.822091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.494990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.200317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.883138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:35.667473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:30.885298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:32.200628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:32.916471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:33.588480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.294884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-28T20:55:34.968493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-28T20:55:40.481030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAnnual_IncomeApplication_TypeApproval_StatusCredit_LimitCredit_ScoreDefault_StatusEmployment_TypeGenderLoan_or_Credit_AmountNumber_of_Active_AccountsPast_Default_CountRegionUtilization_Ratio
Age1.000-0.0020.0000.000-0.006-0.0250.0000.0290.0000.0160.0010.0000.0000.014
Annual_Income-0.0021.0000.0390.026-0.008-0.0110.0260.0000.019-0.0120.0220.0140.0080.010
Application_Type0.0000.0391.0000.0000.0000.0130.0000.0330.0000.0200.0120.0190.0330.000
Approval_Status0.0000.0260.0001.0000.0460.0351.0000.0000.0000.0260.0250.0000.0210.049
Credit_Limit-0.006-0.0080.0000.0461.0000.0140.0460.0220.0080.0020.0110.0120.016-0.003
Credit_Score-0.025-0.0110.0130.0350.0141.0000.0350.0070.000-0.004-0.0100.0000.000-0.028
Default_Status0.0000.0260.0001.0000.0460.0351.0000.0000.0000.0260.0250.0000.0210.049
Employment_Type0.0290.0000.0330.0000.0220.0070.0001.0000.0000.0190.0110.0000.0000.007
Gender0.0000.0190.0000.0000.0080.0000.0000.0001.0000.0000.0000.0080.0180.000
Loan_or_Credit_Amount0.016-0.0120.0200.0260.002-0.0040.0260.0190.0001.0000.0010.0360.026-0.021
Number_of_Active_Accounts0.0010.0220.0120.0250.011-0.0100.0250.0110.0000.0011.0000.0000.000-0.015
Past_Default_Count0.0000.0140.0190.0000.0120.0000.0000.0000.0080.0360.0001.0000.0000.000
Region0.0000.0080.0330.0210.0160.0000.0210.0000.0180.0260.0000.0001.0000.014
Utilization_Ratio0.0140.0100.0000.049-0.003-0.0280.0490.0070.000-0.021-0.0150.0000.0141.000

Missing values

2026-02-28T20:55:35.830642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-28T20:55:36.000825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-02-28T20:55:36.191380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Customer_IDAgeGenderEmployment_TypeAnnual_IncomeCredit_ScoreLoan_or_Credit_AmountCredit_LimitUtilization_RatioNumber_of_Active_AccountsPast_Default_CountApplication_TypeApplication_DateRegionDefault_StatusApproval_Status
0C0000162.0FemaleSalaried1043546.0440.03256804487600.9052Loan2024-02-04UrbanYesRejected
1C0000231.0FemaleStudent1633388.0782.044591611944870.1743Credit Card2023-11-16RuralNoApproved
2C0000359.0MaleSalaried1708828.0604.07969701430130.9510Loan2023-09-21Semi-UrbanNoApproved
3C0000463.0MaleSalaried1006842.0644.09826197022690.1750Loan2023-12-31UrbanYesRejected
4C0000551.0FemaleSalaried622519.0569.07582896646020.9763Credit Card2024-01-24UrbanYesRejected
5C0000637.0MaleSalaried857548.0407.075087311374140.5123Loan2023-08-15RuralYesRejected
6C0000753.0FemaleSalaried1487545.0773.06588952599660.7840Credit Card2024-03-31UrbanYesRejected
7C0000852.0FemaleSalaried1864849.0381.06123888724970.7443Loan2023-11-01UrbanYesRejected
8C0000949.0FemaleSalaried1411399.0NaN26979813457280.5722Loan2024-05-19Semi-UrbanYesRejected
9C0001041.0MaleSelf-Employed234269.0589.046465611879960.6652Loan2023-11-24UrbanYesRejected
Customer_IDAgeGenderEmployment_TypeAnnual_IncomeCredit_ScoreLoan_or_Credit_AmountCredit_LimitUtilization_RatioNumber_of_Active_AccountsPast_Default_CountApplication_TypeApplication_DateRegionDefault_StatusApproval_Status
4990C0499134.0FemaleRetired1454092.0766.018655312653860.4711Credit Card2023-09-03UrbanYesRejected
4991C0499261.0FemaleSalaried417535.0312.04821934814270.9061Loan2024-07-11UrbanYesRejected
4992C0499339.0FemaleSalaried623220.0744.09873873718810.9753Loan2023-07-23UrbanNoApproved
4993C0499450.0FemaleNaN1392116.0523.079427514474710.8762Credit Card2023-07-12UrbanNoApproved
4994C0499525.0MaleRetired165091.0762.0742654030460.5711Loan2023-12-25UrbanNoApproved
4995C0499651.0FemaleSalaried1490957.0320.036488814163260.5641Credit Card2024-07-07Semi-UrbanNoApproved
4996C0499727.0FemaleSalaried1573988.0686.04190716750630.2932Credit Card2023-09-11UrbanYesRejected
4997C0499839.0FemaleNaN1454241.0625.09571744881450.6341Credit Card2023-10-11UrbanNoApproved
4998C0499951.0MaleSalaried994785.0766.0335800800046NaN33Credit Card2023-08-06UrbanNoApproved
4999C0500059.0FemaleSalaried486644.0821.058359212704900.1753Loan2024-07-26Semi-UrbanYesRejected